MBA Dissertation: "Investigation into Programmatic Marketing as a More Efficient, Responsible and Financially Prudent Digital Marketing Practice of the Future"

THE UNIVERSITY OF STRATHCLYDE
DEPARTMENT OF MANAGEMENT
MBA PROJECT
An Investigation into Programmatic Marketing as a More Efficient,
Responsible and Financially Prudent Digital Marketing Practice of the
Future
Nur Isman Bin Tanuri
2014

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An Investigation into Programmatic Marketing as a More Efficient,
Responsible and Financially Prudent Digital Marketing Practice of the
Future
Submitted in partial fulfilment of the requirement of the degree of Master
of Business Administration of the University of Strathclyde
THE UNIVERSITY OF STRATHCLYDE MBA
Nur Isman Bin Tanuri
2014 McWhirter
Benny Sweeney

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Statement of Academic Honesty
I declare that this dissertation is entirely my own original work.
I declare that, except where fully referenced direct quotations have been included, no
aspect of this dissertation has been copied from any other source.
I declare that all other works cited in this dissertation have been appropriately
referenced.
I understand that any act of Academic Dishonesty such as plagiarism or collusion may
result in the non-award of a Masters degree.
Signed …………………….…… Dated …………………….……
1 October 2014

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Abstract
This investigative study seeks to understand on whether programmatic marketing aligns with
current trends, practices and predictions of corporate and marketing practices in the 21st
century. Trends such as ‘leveraging of Big Data’, ‘management as a science’, growth of e-
commerce and mobile are continually discussed in management circles and literature. The
pertinent question is to understand if the industry-at-large is largely adopting or ignoring
programmatic marketing and other complementary technologies.
Another key area of discussion is to recognise on whether programmatic marketing, with its
promises of stricter and efficient marketing spending and highly informed decision-making
abilities, is an answer for corporate organisations to remain competitive and viable in the
marketplace. As with any new technological innovations, this study will attempt to uncover the
contemporary and industry views towards programmatic as well as understand the limitations
and barriers to adopt and implement programmatic marketing in corporate organisations.
Word Count: 15,553 (excluding references and appendices)

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Table of content – MBA Project
1. Project introduction – pg. 9
a. Project purpose
b. Field of study
c. Research and learning objectives
d. Project outline
2. Literature review
a. State of business – pg. 11
b. State of marketing – pg. 13
c. Big Data – pg. 16
d. State of digital marketing – pg. 22
e. Marketing automation – pg. 26
f. Programmatic marketing – pg. 31
g. In summary: Literature review – pg. 35
3. Project methodology – pg. 36
a. Purpose, approach and philosophy
b. Research method and design
c. Limitations of project and research
4. Analysis & discussion
a. The Macro view – pg. 38
b. The Micro view – pg. 46
5. Conclusion & recommendations
a. The Big Picture – pg. 51
b. The Inside Job – pg. 52
6. Summary – pg. 54
7. Personal reflection – pg. 55
8. References – pg. 56
9. Appendix – pg. 60

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Table of content: Literature Review
• State of Business
o Business trends
! Big Data and analytics
! Evolved modes of engagement
! Data-driven business insights
• State of Marketing
o Realising the vision
! Control and influence
o Rise of the CMO
! CMO’s influence on business strategy and performance
! The evolved CMO
! CMO and emerging technologies
! The CMO-CIO partnership
• Big Data
o What is Big Data?
o Definitions
o Uses of Big Data
! Data extraction and analysis
! Personalisation and customisation
! Continuous experiments
! New contractual forms
o The Big Data Promise
o Limitations and concerns
o Big Data for marketing
! Real-time insights, real-time optimisation
! Marketing efficiencies and effectiveness
! The age of marketing automation
• State of Digital Marketing
o The digital trend
o Historical overview
o Significance over traditional practices
o One-to-one marketing & Big Data
o Growth of mobile
o The non-linear, cross-channel consumer
o The marketing data deluge

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• Marketing Automation
o Definition and philosophy of marketing automation
o Why adopt marketing automation
o Implementation challenges
o Marketing automation in practice
! Customer marketing tasks management
! Marketing resource management
! Rise of marketing automation for digital advertising
• Programmatic marketing
o What is ‘programmatic’ marketing?
! Efficiencies and effectiveness
o Programmatic possibilities
! Programmatic defined
! Managing network complexities
o Advancements in programmatic media buying
! Benefits of programmatic advertising
o Challenges in programmatic marketing
! Barriers to adoption
! Data security and privacy
• In summary: Literature review

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Table of content: Analysis & discussion
• The Macro View
o Big Data
! Growth of Internet users
! Growth of mobile devices
! Common uses of Big Data
! Challenges of Big Data
! Implications for marketers
o Digital marketing
! Growth in digital advertising
! Digital ad format wars
o Marketing automation
! Growth in adoption
! Usage levels
! Adoption by company type
! Benefits expected vs practical usage
o Programmatic marketing
! Growth in programmatic markets
! Programmatic premium
! Programmatic philosophy
• The Micro View
o Programmatic positives
! Case study: Ad effectiveness
! Case study: Ad personalisation
! Case study: Mobile audiences at global scale
o Limitations and barriers
! Organisational readiness
! Automation obstacles
! The Data Dilemma
o Negative programmatic
! Resistance to change
! Privacy concerns

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Project introduction

Project purpose
The undertaking of this project is to address questions that have arisen from the relatively
nascent field of ‘programmatic marketing’. While ‘programmatic marketing’ is the buzzword of
the moment, business leaders and marketers remain sceptical regarding its use and result-
producing abilities. Only 23% of marketers surveyed by Forrester and the Association of
National Advertisers have used it and cited lack of understanding as a concern (Hoffman, 2014).
This project seeks to understand on whether programmatic marketing, with its promises of
stricter and efficient marketing spending and highly informed decision-making abilities, is an
answer for corporate organisations to remain competitive and viable in the marketplace. As with
any new technological advancement, the project will also attempt to uncover the opposing
views towards programmatic as well as the limitations and barriers to adopt and implement
programmatic marketing in corporate organisations.
Another area of discussion will be to understand on whether the premise and promise of
programmatic marketing aligns with current trends, practices and predictions of corporate and
marketing practices in the 21st century. Trends such as ‘leveraging of Big Data’, ‘management
as a science’, growth of e-commerce and mobile are continually discussed in management
circles and literature. The pertinent question is to understand if the industry-at-large is largely
adopting or ignoring programmatic marketing and other complementary technologies.
Also, the study will attempt to uncover what are the statistical evidences that are supporting the
market’s confidence in programmatic as the future of digital marketing.
Field of study
The fields of concern in this study are contemporary marketing management, Big Data,
marketing automation and digital marketing (including programmatic). The sum of these three
areas of investigation is vast. However, this author believes that a good general understanding
of these key areas is important to form an opinion on the future of business management,
especially on the digital form of marketing. The literature review will attempt to provide
theoretical and constructive overviews of each of these areas of study.
Research and learning objectives
The overarching objective of this study is for this author to investigate and better understand
this emerging, nascent and complex area of programmatic marketing. The exploration of this
theme is to uncover the future potential and ability of programmatic marketing to align with
current corporate trends of stricter financial spending, the efficient organisation and data-driven
decision-making.

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Additionally, this author also wishes to better equip his professional needs for knowledge and
information in this area. Due to a new career employment with a programmatic marketing firm
(a major technology player in the programmatic media buying space), this author aspires to
pursue intense and deep learning in this subject area. This will allow the rapid increase in
understanding of the domain in order to expertly perform his professional role within a reduced
period of time.
Project outline
This project will firstly incorporate a literature review that will explore themes that concern the
study including current trends in marketing management, Big Data, digital marketing,
marketing automation and programmatic marketing.
Using insights and understanding from the literature review, the research methodology will be
formulated to provide a relevant and organised approach for the collection of secondary data
and additional information required to address the study’s objectives.
Using data and information gathered, the author will provide analyses incorporating previously
uncovered and known insights. A project discussion will follow to answer the project questions
as well as to make possible recommendations or address limitations of adoption or limitation.

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Literature review
State of Business
Business Trends
In Deloitte University’s ‘Business Trends 2013’ research report (Canning et al., 2013), a
number of key trends were highlighted, including how business intelligence is being redefined
with the use of analytics and Big Data, the evolving mode of engagement with customers and
the proliferation of the use of social media data together with first-party and third-party data to
generate business insights in real-time.
Big Data and analytics
Where large, enterprise-sized companies used to have the advantage in terms of vast and diverse
resources, smaller firms are becoming more adapt at disrupting the market through the use of
open-source platforms, cloud computing, social media, and analytics technologies (Lucker et
al., 2013). The emergence of Big Data and analytics has prompted businesses to re-evaluate;
according to Bruno Aziza (2012), “Companies are realising analytics are actually at the centre
of their company, whereas before analytics was just at the edge.”
Evolved modes of engagement
According to Copulsky and Cutten (2013), customers are now more enabled than ever,
especially with the digital revolution. Where marketing through mail-order catalogues,
telemarketing and on-premise displays and promotions are the norm 30 years ago, customers
and consumers today are keener on a do-it-yourself approach to shopping and researching for
products.
Increasingly, this is done through Internet-connected electronic devices, such as desktop
computers, smartphones and mobile tablets. This, in turn, has produced a modern consumer
whose shopping habits are ‘omni-channel’ by nature; there are now plenty of buying options via
various devices. Consequently, marketing touch points (through digital advertising) have also
evolved and varied across these different devices (Hsiao et al., 2012).

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Data-driven business insights
Since the rise of the early social networks such as Myspace, customers are now increasingly
willing to reveal information about themselves including their personal and professional
interests, their social networks and purchasing decisions, either voluntarily or otherwise (Palmer
et al., 2013). This has resulted in a growing abundance of data and insights that are now
available to businesses to tap on for strategic decision-making and, in some cases, making
decisions in near real-time.
Palmer et al. also foresee the increased practice of combining social data with in-house first
party data (such data recorded in financial, CRM or web management systems) and third party
web browser cookie data providers.
In digital advertising, the combination of social, first and third party data sets presents a
powerful arsenal of targeting tools for any advertiser as third party data alone does not provide
the most accurate representation of an online user’s interest or intent.

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State of Marketing
Realising the vision
Control and influence
In the article ‘The Gap between the Vision for Marketing and Reality’, Kotler et al. (2012)
asked ‘Has marketing realised the vision to which its adherents have long aspired?’ In this
instance, the ‘vision of marketing’ refers to the fulfilment of the two core ideas of the role of
marketing: firstly, the concept of ‘marketing mix’ and the ability to control or influence either
all of the classic 4Ps of marketing (product, place, promotions and price) or a blend of each 4P
component. Second is the idea that all marketing decisions should be based on ‘a solid
understanding, based on hard data, of target customers and other stakeholders’.
Using results from IBM’s 2011 Global CMO Study as benchmarks (respondents were asked to
rate their influence of each of the 4P component on a scale of 1 to 5), Kotler et al. indicated that,
beyond the promotion function, marketers do not fully control aspects of the decision making.
According to data from the study, marketers rate their influence over promotions in their
organisations at 4.2. Control of the product element came in at 3.5 while the use of data-driven
analysis for marketing was rated at 3.4. Control of place (3.2) and control of price (3.1) were
also polled.
Tellingly, when the Full-Scale Marketing Index (an overall metric based on the average control
of each of the 4Ps and the use of data) was calculated, CMOs, on average, rated themselves a
3.5. Kotler et al. determined this to be ‘distinctly below the top mark’. In their assessment,
Kotler et al. concluded that some organisations are ‘further away from the (marketing) vision
than others’.
Interestingly, further evaluations of the results showed that CMOs whose organisations have
high control over their 4Ps and exhibit high usage of data for marketing purposes are also ‘the
most forward-looking’. These organisations indicated ‘greater intentions to increase use of
newer tactics such as tablet and mobile applications, social media, e-mail marketing and
predictive analytics.’ Additional analysis also indicated that these companies, based on available
public data, are ‘significantly outperforming the industry’. This implies that a firm’s financial
performance does correlate with its marketing organisation’s high control of marketing mix and
data use.

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‘Rise of the CMO’
The previous insight informs the re-emerging business trend that is the ‘rise’ of the
technologically savvy Chief Marketing Officer (CMO) and his or her increased influence,
importance and involvement in business strategy and within the Top Management Team
(TMT).
CMO’s influence on business strategy and performance
In order to prove the organisational theory that firms who have elevated their CMOs to the TMT
and have a commitment to a consumer-centric strategy will enjoy better economic performance
and shareholders’ value, Abernathy, Kubick and Masli (2013) tracked the stock performance of
large firms within two separate portfolios, one group with CMOs within the TMT and one
without.
Using performance attribution analysis and other measures, the study was able to indicate that
having a CMO in the TMT can have a positive influence on shareholders; wealth, hence
justifying the economic relevance of CMOs. The study concludes that ‘the presence of a CMO
in the TMT results in an annualized excess return of approximately three per cent relative to
firms without a CMO in the top management team.’
B & T Weekly (2014) summarised this sentiment clearly: ‘At the heart of every business
strategy is the customer and as marketing moves towards building relationships with customers,
the role of the CMO is set to become more prominent in the future.’
The evolved CMO
In the piece ‘The Evolution of New Species of CMO’ (2013), Dick Patton and Rory Finlay used
the borrowed term ‘cladogenesis’ to describe changes and expectations in the role of the modern
CMO. Evolutionary scientists explain cladogenesis as the division of a species into 2 separate
species due to radical changes in the environment.
Patton and Finlay see the CMO as currently in the process of this evolutionary phase. Among
the five critical axes where change is taking place, the transformation of a marketing
traditionalist CMO to one that has to embrace digital and be an expert in this realm is crucial as
the ‘digital revolution’ continues to gather pace in both mature and emerging marketplaces.
CMOs must now ‘contemplate the trade-offs between digital vs. traditional marketing expertise’
in order to stay relevant.
Another key area of change is the ‘Innovation Champion vs Shopper Expert’ paradigm. The
classic view of the CMO’s role as an enabler of sales (Shopper Expert) is challenged by the
increasing requirement for the Innovation Champion persona. More emphasis is now placed
upon the CMO role as an encourager and motivator the organisation to continually develop new

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ideas to generate future pipelines. In the process, the adoption of newer and better technologies
is crucial for this to happen.
CMO and emerging technologies
The increased use of Big Data and digital marketing and the transparency afforded by data-
driven digital mediums has also force-transformed the contemporary CMO. Apart from having
to adapt and integrate these new digital knowledge and skills into their marketing strategy
formulation, CMOs are now more accountable in terms of their direct impact on revenue and
their ability to influence business strategy (B & T Weekly, 2014).
Neff (2011) provides an accompanying perspective: the elevated role of the Chief Marketing
Officer is largely due to the dual technological effects of digitisation and globalisation. The
digitisation and globalisation of media, such as Facebook and Twitter, have made it possible
and necessary to manage brands on a global holistic scale.
The CMO-CIO partnership
According to Peterson et al (2010), to harness the promise and virtues of digital marketing
(speed, flexibility, interactivity and accountability) requires a whole new set of marketing
strategies and skills to make it work. This includes the need for a close collaboration between
the CMO and the Chief Technology/Information Officer.
What was once thought of as an ‘uneasy alliance’ or a ‘natural double act’ is now a strategic
initiative that has to be encouraged by forward-looking organisations (Marketing, 2014). While
CMOs’ focus has always been on the customer, brand building and using creativity to drive
revenue (including the use of new and, sometimes, unproven technology), the CIO/CTO’s
mandate is based around IT systems procurement, ensuring connectivity, stability and cost
reduction, reducing operational risks and managing costs.
Given that marketing organisations are making increased investments into digital marketing
with the upsurge in consumer’s media consumption and network connectivity, the more likely
CMOs and CIO/CTOs are expected to develop a shared vision and a collaborative and
communicative working relationship (Marketing, 2014).
This is made even more apparent when IT procurement is required. Capability investments in
social media, mobile, marketing and programmatic ad-buying that involves platform and
infrastructure procurement will require the expertise and agreement of the CIO/CTO (Peterson
et al., 2010). This is certainly crucial for the successful implementation of a Big Data platform,
especially with so many data-producing marketing activities occurring on digital media
platforms.

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Big Data
What is big data?
The explosion of digital data creation can be summarised by an oft-quoted 2011 statement
attributed to IBM that says ‘90% of the world’s data today has been created in the last two years
alone.’
In its archive-possible form, Big Data, also referred to as ‘dark data’, refers to pieces of
unstructured data that can consists of things such as web pages, e-mails, electronic documents,
images and video (Datskovsky, 2013). Crucially, Big Data exists in large datasets in the
magnitude of petabytes, terabytes, exabytes, and zettabytes. This allows analysts to do such
things as spotting business trends, preventing the spread of disease, and combat crime.
In marketing, Big Data most often consists of datasets derived from online marketing activities
that can include e-commerce history, CRM, web traffic and audience data among others.
Definitions
A 2011 McKinsey Global Institute report on Big Data (Manyika et a.l, 2011) defines ‘Big Data’
as follow: “…refers to datasets whose size is beyond the ability of typical database software
tools to capture, store, manage, and analyse.”
Gobble (2013) in her paper ‘Big Data: The Next Big Thing in Innovation’ also offers a similar
practical description: “…data is big when it’s too big for conventional systems to handle it.”
Sam Madden, MIT researcher, offers a similar anecdote: “…means data that's too big, too fast,
or too hard for existing tools to process.” (2012)
In a further explanation, Gobble emphasises that Big Data is not only about size but also refers
to the immense volume of data created, the speed at which this data creation takes place
(velocity) as well as the unstructured and varied nature of the data created. In other words, most
of the data created is not usable; organisation activities using analytical techniques and tools are
required. Examples cited include tweets created by users of social messaging service, Twitter,
automatic data created by appliances or devices connected to a network, such as a Tesla
automobile or a GPS watch worn by a runner.
Uses of Big Data
In his 2014 paper, ‘Beyond Big Data’, Hal Varian, Google’s Chief Economist, explains that
computers that were once installed to manage business transactions and their resulting
accounting activities are now much more capable to handle other uses. These uses include:
• Data extraction and analysis
• Personalisation and customisation
• Continuous experiments

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• New contractual forms due to better monitoring
Data extraction and analysis
In data extraction and analysis, progress in technology has allowed for the collection, storage
and manipulation of data in massive magnitudes. This has also allowed for developments in
newer or updated methods of data analysis. The relatively new fields of ‘predictive analytics’,
‘data mining’ and ‘data science’ have enabled the deeper analysis of vast data sets.
Varian argues that, although data extraction and analysis are typically the talking points when
‘Big Data’ is discussed, the other key uses merit even more focus and will gain in importance in
time.
Personalisation and customisation
A digital database’s inherent ability to create highly specific segment populations and audiences
has always aided the personalisation and customisation required in marketing communication
campaigns. However, analytical advances in Big Data have also allowed for marketers to
connect, in real-time, with micro-segmented online audiences that are most likely to be
receptive to specific promotions or advertising messages (Manyika et al., 2011).
Another of Big Data’s biggest advantages for marketing is in its ability to forecast and predict
future behaviours. By seeking repeated patterns and insights in large active data sets, marketers
will be able to deliver pinpoint-accurate advertising messages to consumers. For example, a
consumer who is keyword-searching for a used Ford car in a search engine, such as Google’s or
on an auction website, can be “retargeted” elsewhere on the internet (for example, on Mail
Online) with a highly-personalised display advertisement that features such a Ford car, down to
its exact model or year of manufacture, or even the nearest dealer to test drive said vehicle.
Continuous experiments
Big Data also affords the ability to run experiments in order to test and determine whether
outcomes from a series of events are a matter of causality or correlation (Varian’s examples
include a marketing manager’s take on the perceived impact of advertising spend on sales:
“Every December I increase ad spend, and every December I get more sales.”) In a digital
online environment, experiments can be done on the cheap, automated and at scale.
Varian espouses: “In order to understand causality, you have to experiment. And if you run
experiments continuously, you can continuously improve your systems.” Manyika et al. (2011)
also corroborate: the ability of Big Data to collect more accurate and detailed performance data
should significantly enable the ability to setup experiments and measure and analyse variability
in performance in almost real-time situation.

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New contractual forms
The spill over effects of behaviour-based data, where every action creates a data point that
translates into a recorded transaction, can now be easily monitored and verified in a database. A
simple example would be a click on a banner advertisement on a website (Varian, 2014).
Together with the availability of advanced storage and archiving capabilities offered by Big
Data technologies, an increased number of contractually binding computer-mediated
transactions are now taking place in many commercial situations. Where once it was not
possible for print media publishers to guarantee payment from advertisers based on a set
number of advertisement viewers, the total recorded and verified clicks on online banner
advertisements can now be used as a basis of total payment.
Combining these computer-mediated transactions with automated and algorithmic-driven results
in a large number of new business models that are based around trust and verification, aided by
Big Data computers (Manyika et al., 2011). Start-up businesses, such as AirBnB and Uber, are
entirely based on this “trust economy” where trust, created by computer-mediated identity-and-
reputation verification, permits for the mutual exchange of services and payments.
The Big Data Promise
Manyika et al. (2011) believes that ‘we are on the cusp of a tremendous wave of innovation,
productivity, and growth, as well as new modes of competition and value capture—all driven by
big data as consumers, companies, and economic sectors exploit its potential.” The
counterargument suggests that businesses and organisations have always exploited data with the
advent of modern information and communication technologies; therefore, this should not be
something new.
However, Manyika’s 2011 McKinsey report suggests that research showed the scale and scope
of this Big Data phenomenon is set to be the driver of value creation and will be the new arena
of competition for companies. The confidence in this prediction is substantiated by the
availability and improvement to data analytics software that are now capable of applying
sophisticated analytical techniques, powered by growing computing horsepower.
This is further aided by the widespread trend of growing digital technology users, connected
devices and sensors that are capable of generating, communicating, sharing and accessing data
over digital networks, through wired or wireless means.
This is evidenced by the statistics such as the following: in 2010, 60 per cent of the world’s
population (or more than 4 billion) are using mobile phones, of which, 12 per cent utilises a
smartphone. The smartphone itself has a penetration growth of 20 per cent a year. Additionally,
networked sensor nodes, estimated at 30 million, are now present in the automotive,
transportation, retail, industrial and utilities sectors (Manyika et al., 2011).

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Limitations & concerns
The lack of expertise in Big Data remains a stumbling block for most organisations. Gobble
(2013) cites a McKinsey report: in the United States alone, a predicted shortage of 190,000
people with deep analytics skills, such as statistics and machine learning, will impact
organisations’ ability to exploit Big Data. Varian (2014) also echoes a similar refrain. Although
the potential of Big Data is enormous, he believes that ‘many organisations have interesting
data but no internal expertise in data analysis’. Gobble further adds to this narrative: There is an
equally huge shortage of managers with the necessary experience and expertise to understand
and make innovative decisions off Big Data analyses.
Gobble cautions: making big data work to drive innovation or to reshape innovation processes
will not be easy. Citing a historical case study of CIA’s work in collecting and analysing
satellite data in the 1970s, the sheer volume of data amassed proved to be a challenge for the
agency. While data production and collection were routine, analysing for insights remained an
Achilles’ heel to the operations: the obtained insights were mediocre at best. Gobble forewarns
that, as in the past, “the technology for producing data has grown exponentially, but tools and
techniques for extracting value from it have lagged.”
Additionally, Chui et al. (2010) warns, although the proliferation of sensors and the imminent
age of ‘Internet of things’ will contribute to a new level of automation in data collection,
transmission and analysis, this deluge of data and information will also likely contribute to
issues such as privacy, security, intellectual property theft and data mismanagement due to a
deluge of information and data.
For individuals and also of corporations and governments, privacy remains the biggest concerns
around the collection and usage of Big Data. According to Manyika et al., (2011), ‘many
citizens around the world regard this collection of information with deep suspicion, seeing the
data flood as nothing more than an intrusion of their privacy.’ However, Manyika et al.
counters that, despite the apprehension, there is strong proof that private commerce and the
public sector can gain significant economic benefits and value from the utilisation of Big Data
and the technologies associated with it. These benefits include, and not limited to, enhanced
productivity, increased competitiveness among firms, increased efficiency and quality and the
creation of economic surplus for consumers. In an estimate, Manyika et al. reckons “a retailer
using big data to the full has the potential to increase its operating margin by more than 60 per
cent.”
Big data for marketing
Real-time insights, real-time optimisation
In his article ‘Making Big Data Marketing a Reality: Build or Buy?’, Duane Edwards (2013)
articulated his views on why Big Data Marketing is very relevant and essential for today’s

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marketing organisations. In particular, Edwards vouches for the ability of Big Data initiatives to
provide opportunities for marketers to make real-time decisions and react to customers’
behavioural insights. This, he states, will move marketers from their conventional dependence
on ‘…aggregated customer metrics that are the cornerstone of traditional approaches to
marketing’.
Edwards also argues that Big Data investments in marketing can be easily justified through the
following recognition process: the better a business understands the behaviours of its customers,
the better able the business will be able to provide personalised and customised interactions.
Theoretically, this will allow a better chance of success for each key performance indicator
(KPI) employed by the business to measure marketing activities. These KPIs include revenue
generated, customer churn rates, usage levels, promoter score etc.
Marketing efficiencies and effectiveness
Manyika et al. (2011) state that the use of Big Data will lead to substantial productivity growth.
Organisations will achieve improve efficiencies and effectiveness through Big Data-enabled
systems and processes and thus allow operations to “do more with less and to produce higher-
quality outputs.”
Newberry, Wessner and DeCarlo (2008) further illustrate this in explaining the ‘Marketing
Operation’s Iceberg’: “Research has shown that as much as 80 per cent of marketing resources
are spent on the back-room functions that are required to create content and manage the
channels of communications to the customer.” This is where the capabilities of Big Data can
contribute to the transformation of marketing operations to one that requires fewer resources.
Doing so, Newberry et al. contend, allows a bigger portion of the marketing investment to be
“transitioned to customer-facing activities that can yield a huge ROI”.

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The age of marketing automation
Biegel (2009) offered that marketing automation has become ‘critical to overall marketing
management’. Advances and development in technologies that contribute to the automation of
marketing processes and activities has allowed for marketers to achieve increased levels of
marketing effectiveness and efficiency. In Biegel’s view, marketing automation is the
cornerstone of digital marketing.
The key to the growth in marketing automation’s deployment is the technological developments
made in Big Data (Zhang & Zhu, 2014). Big Data capabilities, such as gaining instantaneous
insights through algorithmic analyses and the ability to act upon these insights in real-time, have
further resulted in the development of the ‘intelligent precision marketing’ field and put into
practice. In Zhang and Zhu’s e-commerce example, marketing automation has allowed
marketers to intelligently study the marketplace in real-time, based on customers’ behaviour and
actions and react accordingly (such as offering an immediate deal if there is a danger of
shopping cart abandonment). This results in better conversion and eventually lowering
marketing costs as no additional resources will be needed to remarket to these customers again.

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State of Digital Marketing
The digital trend
Peterson et al. (2010) captured the essence of the digital shift: “Consumers of media have seen
the digital light, and they are shifting in ever-growing numbers to all kinds of digital media
channels -- the internet, electronic messaging, online search, the social web, blogs, podcasts,
mobile communications and gaming platforms”.
The impact of this, according to Peterson et al. (2010), is that marketing budget is being shift
towards digital media and channels and that marketers are “abandoning traditional media at a
shocking rate”. A key driver of this is because digital marketing promises “intelligent
individualised consumer targeting” at a cost that is far lower than traditional media.
In another observation, online and mobile are the only advertising segments expected to grow in
the near future (Peterson et al., 2010).
Historical overview
Digital marketing truly came to the fore with the invention and arrival of the World Wide Web
(WWW) on the Internet. In October 1994, the first interactive (clickable banner) display
advertisement appeared on Hotwired.com on behalf of the advertiser, AT&T (Winterberry
Group, 2013).
Email marketing, however, started much earlier. The first commercial email (an offer to
purchase a DEC computer) was delivered to 400 Arpanet users in 1978 by Gary Thuerk while
Hotmail arrived in 1991 and any individual an opportunity to own a free web-based email
account (Optyn, 2013).
Contrary to popular knowledge, search engine marketing first arrived with the launch of
GoTo.com’s search advertising keyword auction program in 1998 (Bourne, 2013). Yahoo! and
Google launched their search advertising programs in 2000.
With advances in the internet, computing power and connected mobile devices, other forms of
online or digital marketing have emerged since and have extended beyond the traditional
desktop, including social media advertising and mobile advertising (Bourne, 2013).
Significance over traditional practices
Peterson et al. (2010) puts across a set of reasoning on why digital marketing is truer to the
‘goal of every marketing effort’ which is to ‘drive profit’ for an organisation. Peterson et al.
outlined the following as the cornerstones and aspirations of any profit-driven marketing
strategy:

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• Build an analytical understanding of individual consumer's behaviour, needs and
communication/media usage patterns
• Leverage decision-support tools that allow marketers to target customers 24/7 on the
right channel, at the right time and with the right personalised and customised message
• Build embedded processes that can orchestrate marketing messages and offers across
the different channels, setting relevant targets and measuring results.
• Create an aligned organization that develops leadership, structures, skill sets and
incentive systems geared towards the digital world
In summation, Peterson et al. believe that digital marketing triumphs traditional marketing
practices such as advertising billboards, print and TV advertising in meeting each aspect of the
profit-driven marketing strategy previously mentioned as well as providing a deeper
understanding of “where those profits come from”.
One-to-one marketing & Big Data
In the book ‘One to One Future: Building relationships one customer at a time’, Peppers and
Rogers (1993) outlined the vision and promise of a one-to-one marketing world, where
marketers eschew the mass-market and focus on creating one-to-one relationships with
customers. Through digital marketing, a radical concept in 1993 has slowly become a reality.
Edwards (2013), in outlining the technological challenges faced by both marketers and IT in
building a Big Data solution for marketing, describes the already-possible approaches to one-to-
one marketing via digital means:
• Through the collection, storage and analysis of captured customer data, the marketer
now has the ability to understand who the customer actually is and how he or she
behaves
• With these insights, the marketer also has the ability to react in real time and in an
automated manner to the behaviour of each customer
• In addition, with the Big Data technology and infrastructure that is presently available,
the marketer has the ability to monitor at scale the dynamic behaviours of millions of
customers all at the same time
Even more specifically, Manyika et al. (2011) identified the possible digital marketing
techniques that are made available by using Big Data. These include:
• Cross-selling
• Location-based marketing
• In-store behaviour analysis
• Customer micro-segmentation
• Sentiment analysis

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• Enhancing the multi-channel customer experience
Growth of mobile
With the growth of mobile device usage, the proliferation of social and media channels and the
ever-increasing amount of media created and consumed, the digital marketer is confronting a
complex change in the consumer landscape.
In its report, The 2014 Digital Marketer, Experian (2014) reports that 41 per cent of smartphone
users indicated that their mobile phone is their primary way of accessing the internet. This
increased use of mobile devices as a principal tool to access the internet and media is triggering
a wave of fundamental change in how digital marketing is practised. In its report ‘Programmatic
Everywhere’, Winterberry Group (2013) underlines this change in two parts; one is the inability
for digital marketers to ‘drop’ the traditional ‘browser cookie’ in mobile devices for re-
marketing and tracking purposes (Microsoft (2014) describes the cookie as a small file that
websites deliver to a website user’s hard drive during a site visit that stores browsing data, visit
history and even personal preferences and information. Desktop websites use cookies to deliver
a personalised browsing experience.)
The non-linear, cross-channel consumer
The other important shift identified by the Winterberry Group (2013) is the new cross-device
audience identification challenge. With the use of multiple devices and immediate access to
multiple information and media sources, Experian (2014) explains that consumers are no longer
traversing exclusive “marketing channels” as in traditional marketing thinking.
They also do not think of themselves as engaging in “the buyer’s journey” and therefore do not
fit in any “buyer persona” framework. Rather, modern consumers’ attention is more fragmented
and that they are only searching or shopping for relevant items that are of interest to them at a
particular point in time.
Hence, the new “buyer’s journey” is described as non-linear at best, and tracking, segmenting
and generating insights from these modern consumers require ingenuity on the part of marketers
as well as rapid adoption of new technology. Therefore, Experian (2014) suggests that
organisations focus on the following areas in order to tackle the cross-channel consumer
paradigm:
• Big-picture commitment
• Processes and success measures
• Real-time customer insight
• Flexible technology
• Harnessing data and analytics

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The marketing data deluge
The fact that consumers are constantly moving from device to device and from one digital
‘channel’ to another, an ever-increasing amount of Big Data and information are left behind
(Experian, 2014). However, this explosion of data is both bane and boon to the digital marketer.
The most apparent advantage of the marketing data deluge is that marketers are now able to
better understand the needs, desires and preferences of their customers which allows marketers
to ensure “better customer engagement and messaging strategies that lead to loyalty and
advocacy” (Experian, 2014).
On the other hand, the surge in data creation presents a new challenge in itself: how do
marketers organise, manage and gain usable insights from Big Data?
Additionally, Experian (2014) believes that it is even more crucial now to be able to link
multiple data assets together in a centralised location. This will allow the creation of an even
more accurate view of the customer to form meaningful marketing engagements. Therefore, the
challenge for marketing organisations is to find a solution on how to manage the inflow of
large-scale data and to react to on-the-fly customer analysis in a real-time manner.

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Marketing Automation
Definition and philosophy of marketing automation
The Winterberry Group’s definition of marketing automation (2009) runs as follow:
Marketing Automation: the utilisation of marketing technology solutions to automate marketing
processes including (though not limited to) planning, budgeting, segmentation, database
management, analytics, creative execution, asset management, campaign execution, lead
management and reporting. These marketing technologies are a combination of software,
networks and hardware that allow the inputs, processing and outputs of marketing and business
information and content.
Much earlier in the century, Robert Shaw, in his 2000 piece ‘Marketing Automation – Myth or
Magic?’, sets out to define what marketing automation is going to be and what it should not
become. In his opinion, ‘marketing automation’ should not be seen as a replacement of
marketing professionals as he believed marketing is a creative process and that ‘it is patently
absurd to suggest that robots will soon be challenging our most creative people’.
Shaw iterates that the value of computer software (the foundation of marketing automation) is in
its ability, through algorithmic logics, to recognise patterns and manage complexity. A system
of such software will have the ability to control the complexity of thousands of customer
relationships as well as deliver the ‘right offer to the right customer at the right time’. Along
side this, marketing automation technology should also be able to accurately identify threats and
opportunities through an ‘always-on’ observation of the (digital) marketplace.
Further, Shaw proposes that, in deciding how to use marketing automation technology,
marketers must carefully align the objective of the use of this technology to the fundamental
business goal. This goal, as defined by Shaw, is ‘to maximise shareholder value’.
Shaw believes that by having marketing align as such, it positions marketing as ‘the most
important driver of sustained growth’. To fully achieve this, the strategic blueprint for
marketing to follow is as such:
1. Gain insights from the market and customers in order to deliver a sustained flow of new
opportunities. This is done by replacing worn out products and services with new ones
that customers would actually want to purchase.
2. By using customer profitability analysis, marketing can build long-term relationships
with profitable customer base and downgrade the focus on less profitable customers.
3. Using customer defection analysis, marketers can find out the root-causes of defections
and ensure revenue volatility and risks to the business is kept to a minimum or avoided

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4. Finally, marketing, through the use of branding activities (ideally automated) and
customer value positioning, can ensure premium pricing is maintained at all times
Marketing can achieve all of the above, Shaw argues, by developing and deploying of these
powerful marketing automation tools. Maintaining the operations the operations of the
marketing automation technology will also ensure the cyclical nature of customer value creation
will be sustained over a long period of time.
Why adopt marketing automation
The appeal of marketing automation varies from marketer to marketer and depends on actual
marketing needs (Biegel, 2009). However, the modern business environment that marketers
operate in is typically the cause for the adoption and use of marketing automation. These
include the increased creation of data, fewer resources, restrictive corporate environments and
also increased accountability.
Consequently, many marketers are focused on optimising their returns on marketing investment
(ROMI) to improve marketing effectiveness and efficiencies. In their consideration for a
marketing automation investment, Newberry et al. (2008) proposes that the solution should
meet the three most common categories of business drivers: increased productivity, increased
accountability or visibility and cost savings. With careful consideration, this can result in an
increase in revenue, profit and market share while maintaining overall marketing spend.
Marketing operations iceberg
Another motivation to adopt marketing automation, as proposed by Newberry et al. (2008) is to
counter the ‘marketing operations iceberg’ conundrum. Research has shown that, while most
marketing technology investments are made on customer-facing applications and services, not
as much investment is made on back-room solutions.
It is shown that as much as 80 per cent of marketing resources are spent on behind-the-scenes
functions and activities that are required to operate a marketing organisation and its output.
These activities include content production and management of channels of communications.
With careful investment, this iceberg-like situation can be better managed, where possible, by
the automation of marketing functions and, especially, that of data handling.

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Implementation challenges
Peterson et al. (2010) identified the ‘daunting’ challenges a CIO might face when architecting a
comprehensive marketing automation system, ‘from the data architecture and the content
management platform to the individual online and offline channel platforms.’
Some of the key challenges include the need to ‘create a single view of all their customers out
of a bewildering variety of constantly updated data sources, from legacy systems to online
customer interaction data’.
Also, the system’s architect must utilise the collected data and information ‘to make specific
offers to individual customers based on their value to the company’, before and after a sale has
happened.
The system must also be able to integrate workflows from every offline and online channel as
well as coordinate when and how communication should take place with each individual
customer, either outbound or inbound.
Another component to implement would be a platform that can automate ‘the process of
publishing a consistent set of marketing messages and content through every marketing channel,
from classic TV spot to Facebook app to YouTube video to Google AdWord to blog entry’.
Marketing automation in practice
In practice, marketing automation comes in varied forms and implementations. The architecture
of marketing automation also varies from one organisation to another.
Customer marketing tasks management
Marketo, a marketing automation technology, defines the availability of these practices in a
single platform to be essential in an enterprise-class marketing automation solution for
managing commercial interactions with prospects and customers (Miller, 2013):
• Email marketing
• Landing pages
• Campaign management
• Marketing programs
• Lead generation
• Prediction/Scoring
• Lead management
• Customer relationship management (CRM) integration
• Social marketing
• Resource management
• Marketing analytics

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While the above practices are possible at small volume without automation, Marketo
believes that technology is required to manage marketing automation once scale is
involved, especially when large quantities of data is involved.
Apart from efficiencies and time saving through a singular platform, Marketo (Miller,
2013) reasons the need for a marketing automation system for the management of the
following tasks:
• Lead nurturing, lead scoring and lead lifecycle management:
o Marketo states that only 20% of leads are ready to purchase when they first
arrive. Therefore, a disciplined process, such as lead nurturing, is required to
develop qualified leads into sales-ready prospects.
• Retain and extend customer relationships:
o Marketing automation takes care of customer retention and the deepening of
relationships between both parties. The objective of this is to prepare the
customer for cross-sell, up-sell and customer loyalty and retention.
• Build alignment with sales
o By integrating a marketing automation system with a CRM system, more
visibility can be provided throughout the sales cycle process. Through alerts,
leads that score high on both demographic and behavioural traits can be
delivered to sales persons to follow up in a timely and relevant manner.
• The ability to prove and improve marketing ROI
o With a transparent and integrated overview of both the marketing and sales
processes, marketing has the ability to report on which activities are producing
results. Additionally, it is an opportunity for the CMO to report on marketing’s
actual impact on revenue.
Marketing resource management
Another component of the marketing automation suite, as defined Newberry, Wessner and
DeCarlo (2008), is the marketing resource management (MRM) system. Within the umbrella
MRM system is a family of software applications with a fundamental purpose of making
marketing processes more efficient through the automation of the flow of marketing-related data
and a capability to measure the result of a particular process.
The types of marketing processes that can be automated include:
• Budget and resource allocation
• Materials production
• Marketing calendar management
• Creative review and approval

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• Offer development and management
• Vendor/agency management
• Digital asset consumption
Essentially, an ideal MRM system will be able to support marketing planning, design and
production work. In its entirety, the MRM should be able to unify the various marketing
processes together in a consistent and automated manner and thus addressing the ‘marketing
operations iceberg’ issue.
Rise of marketing automation for digital marketing
Another key area of discussion is the impact of marketing automation on digital media buying
or what is generally known in the industry as ‘programmatic advertising’. Yao (2013) describes
this emerging practice as the replacement of ‘two people negotiating over buying and selling of
advertising’ with computers. Vranica (2013) adds that programmatic is a ‘major departure from
the traditional ad-buying process, which for decades has largely relied on relationships between
buyers and sellers.’
The Winterberry Group (2013) quoted a 2012 description of programmatic made by thought-
leading publisher AdExchanger: ‘similar to programmatic stock trading insofar as buying
happens as the result of a computational proxy bidding on behalf of human masters’.
Although the use of automation in media buying began as a way for web publishers to ‘sell
lower value ad inventory’ (Vranica, 2013), programmatic is gaining popularity with publishers
and advertisers as the concept of using automated systems in the process of buying and selling
advertising inventory gains a foothold in the marketplace.

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Programmatic Marketing
What is ‘programmatic’ marketing?
In summary of their paper ‘Marketing resource management: An investment that can energise
your customer management strategy’, Newberry et al. (2008) proposed that ‘marketing
efficiency involves saving time and money through expeditious processes and fiscal
responsibility.’ Adding on, Newberry et al. believe that ‘marketing effectiveness ultimately
involves an improved ability to impact consumer behaviour’ and that this can be best served by
‘properly leveraging technology’.
With the preceding concepts in mind, the proponents of programmatic argue that the solutions
to these objectives can be found within technological advancements in both Big Data solutions
and digital marketing, specifically ‘programmatic’.
Joe Zawadzki, CEO of MediaMath (a programmatic advertising technology company), defines
‘programmatic’ simply as: ‘The use of technology to automate processes and the use of math to
improve results’ (Ebbert, 2012).
John Cordone, CEO of [x+1], expands this further: “Programmatic marketing requires a more
comprehensive platform that can execute complex logic across a variety of systems, including
website content management, email, call centre enabled chat, mobile apps and CRM systems
(Ebbert, 2012).
Another point of view is offered by Juliusz Michajlow (2014): “Programmatic marketing is a
human-made technology which makes single marketing decisions on our behalf so that we
achieve our goal. But it is not the machine that sets the goal, but people.”
Efficiencies and effectiveness
The idea that programmatic promotes cost-efficiency through automated and efficient
operations and data-driven marketing is also supported by Meredith Levien, Advertising
Executive Vice President, New York Times (Digiday, 2013). Levien believes that programmatic
solves the ‘transactional middle’ problem of the resource-intensive, ‘people-led RFPs’ in media
buying and that marketers’ objectives are best served by leveraging on machine-based
programs, either in whole or in parts.
If marketing effectiveness is measured in increased revenue and profit, Gartner (2014) estimates
that companies who deploy programmatic technologies in marketing to existing customers can
expect revenue increases of as much as 20 per cent. Similarly, IBM’s Centre for Applied
Insights, through their 2013 Global Marketing Survey, found out that firms that utilise
“forward-thinking engagement platforms” such as programmatic technologies had revenue

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growth rates that were 40 per cent higher than their peers and had profit growths that were
double of their peers (Wachter, 2014).
Programmatic possibilities
While Howarth (2014) sees ‘programmatic’ as a broad field of automated marketing activities
that includes the generation of targeted emails in response to an abandoned e-commerce
shopping cart, defining programmatic in the market place is challenging. Due to its complexity,
a number of descriptions have been offered for what is ‘programmatic’.
Defining programmatic
According to Michajlow (2014), some prevalent misconceptions of programmatic include
‘programmatic is real-time bidding (RTB)’, a form of ‘remarketing’ or ‘merely automation’.
Michajlow, however, is certain that programmatic marketing is ‘so much more’ and, as a
technology stack, it consists of the following: marketing automation, dynamic landing pages,
personalised emails, product recommendations, remarketing and RTB, premium advertising
space, advertising creator, audience buying, conversion optimization and advanced analytics.
The use of all or a combination of these technology subsets will allow the optimisation of digital
marketing undertakings.
Michajlow also offers that the key differentiator between ‘programmatic’ and ‘marketing
automation’ is the use and analysis of data. Where ‘marketing automation’ is the triggering of
automated marketing activities or responses that have been pre-programmed, ‘programmatic’
uses the decisioning and prediction capabilities of machine algorithms to optimise for marketing
performance.
Managing network complexity
The need for programmatic practice in advertising is also fuelled by the fact that the Internet has
become a very complex marketplace, says Michelle Said, (2014). This is the result of the
introduction of advertising servers, advertising exchanges, advertising networks, retargeting
services, data suppliers and other tools into the online advertising ecosystem. Said explains
further: “What had once been a simple transaction between advertiser and (media) publisher
was filled in with other options to bring marketers even closer to their consumers”. With
“literally trillions of opportunities available for advertising”, the time-consuming, inefficient
and traditional task of tracking ad campaigns manually was now made impossible.
Advances in programmatic media buying
The Winterberry Group (2013) provides a strategic viewpoint of programmatic media buying
and described it as ‘a holistic approach through which media buyers and sellers may align
organizational processes with automation technology in support of ongoing, channel-agnostic

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customer engagement (and to allow for the continuous optimization of that effort as business
strategies evolve)’.
Benefits of programmatic advertising
Said, in ‘The Evolution of Online Marketing’ (Said, 2014), distils the value of programmatic
buying as the “ability to match marketers to audiences, at a large scale, in real-time, with the
ability to track and verify sales instantly.” Cuddeford-Jones (2013) concurs: “Programmatic
buying's benefit is to place ads where the customer is, instead of where the brand presumes
them to be”. Doing so allows marketers to exploit a consumer’s wide and varied online activity
across multiple devices and media channels, instead of restricting advertising spend on a narrow
set of websites that is considered a fit for the advertiser brand.
Programmatic media buying is also lauded for its ability to work across multiple digital media
channels (New Marketing Institute, 2014). Using a demand-side advertising platform (DSP), an
advertiser will be able to access advertising inventories such as display banners, mobile, social
media and video within a single platform thus enhancing the advertiser’s ability to market to the
cross-channel consumer. According to eMarketer, the buying programmatic TV advertising is
also growing, although it is still in its infancy (Fisher, 2014). This is driven by the growth in
ownership of “smart TVs” that are individually addressable through Internet connectivity.
Challenges in programmatic marketing
The main challenges in programmatic marketing are ensconced in two different areas (Magna
Global, 2013). One is the limited understanding and adoption of programmatic and automated
marketing techniques by publishers, agents and advertisers. The other set of challenges for the
growth of programmatic pertains to strict privacy laws and the uneasiness of consumers in
matters that relate to data security and privacy.
Adoption
The Winterberry Group’s ‘Programmatic Everywhere’ report (2013) highlighted a few of these
obstacles for the adoption of programmatic by organisations on both demand and supply sides
of online marketing.
For publishers, those who resist programmatic are essentially attempting to preserve the legacy
ways of doing things. These include media sales professionals who view the adoption of
programmatic as a ‘threat’ to their livelihood. Publishers also feel that programmatic is reducing
the margins that their premium inventory has traditionally enjoyed.
On the demand end, ‘brand safety’, ‘lack of process transparency’ and ‘data governance
concerns’ are some of the reservations of advertising buyers. Additionally, advertisers also
voiced internal capability concerns of internal resources to comprehend and utilise

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programmatic. Another legacy concern of advertisers is the proliferation of advertising fraud
that includes fake site visitors and URL link clicks.
Data privacy and security
In ‘Big Data, Big Brother, Big Money’ (2013), Lesk highlighted the wide-scale use of private
data in commerce in the US. It is claimed that ‘huge amounts of personal data are sold
constantly’ by companies like Equifax, Experian and Transunion, each of which has annual
revenues that exceed a US$1 billion. Lesk underscored that firms’ ability to collect personal
data are usually established when users or consumers waive their personal rights by accepting
use licences that accompany services or products online.
While the US and a number of markets, such as the UK, Netherlands and Australia, are seeing
rapid growth and adoption of programmatic, marketers are faced with numerous obstacles in
large modern consumer markets such as Japan, Germany, Spain and China (Magna Global,
2013). These include strict privacy laws that restrict the use of behavioural data for marketing
and targeting purposes.
Forrester Consulting (2014) attributes the rise of privacy concerns among individual users or
customers due to the volume of data created and collected on them that arose from online and
transactional behaviours carried out on the Internet. Moreover, as the quality of data collected
has improved, so has the sensitivity. Furthermore, the ability of Big Data infrastructures to
connect different sources of data together to create a consumer’s profile is contributing to the
anxieties.
Data security is an equally important concern. Manyika et al. (2011) are of the opinion that
protecting data security through technological and policy tools will become more essential
especially with the increasing incidences of data breaches such as the recent celebrity photos
hacking scandal that was allegedly attributed to the use of Apple’s iCloud data backup service
(Lewis, 2014).

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In summary: Literature review
The literature review has shown supporting evidences that adopting programmatic marketing
capabilities is in line with the needs of firms who want to remain competitive and viable in the
marketplace.
There is an overwhelming indication from authors and experts that the use of Big Data,
analytics, marketing automation and programmatic marketing techniques will support the
CMOs’ agenda and thus preserve or even enhance their relevance in corporate organisations.
The literature review has also shown the close relationships between each area of discussion. In
summary, the growing influence of CMOs in organisations can be attributed to technological
advances in marketing automation and programmatic techniques. This is made possible with the
Big Data age. The continuous collection, storage and analysis of multiple sources of data, such
as online behaviour, CRM, mobile and e-commerce, creates a value loop that informs the
decision-making of the CMO and the marketing organisation.
Further statistical and qualitative research is now required to look for evidences to evaluate
actual performances of organisations that have adopted these technologies and its impact on
organisational efficiency and effectiveness.

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Project methodology
Purpose, approach and philosophy
In order to determine and formulate responses to the project questions, this study will utilise a
research component to deliver additional supporting evidences on observations that have been
provided through the literature review.
By looking at both macro and micro views of the general market’s involvement with
programmatic marketing, this study will attempt to look at:
1. [Macro view]: The general industry’s sentiments on programmatic marketing, Big Data
and marketing automation through trends and forecasts.
2. [Micro view]: The involvements and experiences of corporate organisations in adopting
and implementing programmatic marketing strategies.
The research will gather information and data to address the following research questions:
1. [Macro view]: Does programmatic marketing align with current trends, practices and
predictions of corporate and marketing practices in the 21st century?
a. What are the current rates of adoption or implementation of programmatic
marketing and other related technologies, such as Big Data and marketing
automation, in corporate organisations?
b. What are the statistical evidences that are supporting the market’s confidence in
programmatic as the future of digital marketing?
2. [Micro view]: Is programmatic marketing a solution for corporate organisations to
remain competitive and relevant in the marketplace?
a. What are the results or progress that have been acquired with the
implementation and adoption of programmatic marketing?
b. What are the limitations and barriers to adopting and implementing
programmatic marketing in corporate organisations?
c. What are the negative sentiments or views (if any) that have arisen with the use
of programmatic?
The forms of research data and information required for the two views for analysis and
discussion will be as follow:
1. [Macro view]: Statistical reports and analysis provided by secondary data sources
pertaining to the market and industry

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2. [Micro view]: Corporate case studies of adoption and implementation of programmatic
marketing strategies; Use cases of utilisation of programmatic marketing techniques in
the marketplace
Research method and design
The data and information for this research will be collected using available secondary data and
information sources that are relevant to the industry.
The sources of research data and information for this study will include peer-reviewed research
papers, case studies, published articles and annual reports of key companies in the industry.
Other key sources of data are statistical market studies done by independent market observer
organisations close to the industry such as the Interactive Advertising Bureau (IAB), eMarketer,
Forrester and Winterberry Group.
Limitations of project and research
Considering the objectives of the study, budget, human resource and time limitations for this
project, this author intend to predominantly work on using existing secondary data and
information that is available in online research databases or public domain.
Due to the “embryonical” nature of programmatic marketing, its sub-industries and practices,
there is a possibility that research done in this area is limited and that statistical data may not be
easily available. However, the author is confident that, utilising careful investigative methods
and a thorough research of available information sources, an adequate body of data and
information can be acquired to provide research data and information for the study.
Also, making inferences on past studies and researches on the online advertising and branding
industry and topics that are related to the practices of digital marketing can provide enough
material and information to produce a meaningful and insightful project.
While “programmatic marketing” itself may not be a fully defined area of study and practice,
various forms of documentation pertaining to ‘programmatic marketing’ services and platforms
are in evidence throughout numerous academic and corporate literature. Therefore, it can be
argued that programmatic marketing does exist as a form of a corporate organisation’s
commercial activity.

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Analysis & discussion
The Macro View
Programmatic marketing’s alignment with current trends, practices and predictions
of corporate and marketing practices in the 21st century
In answering the first of two research questions outlined earlier, this project took the approach
of a macro view of the world’s trends, practices and forecasts in both technologies and business.
The approach to this is to look at research data, facts and analyses made available in relevant
publications, research organisations and practicing organisations.
Big Data
Environmental evidences contributing to the growth of Big Data creation also support the
Edwards’ (2013) notion that exploitation of Big Data is ‘essential for today’s marketing
organisations. Furthermore, Manyika et al.’s (2011) idea that the collection, storage and use of
Big Data in making marketing decisions is strongly supported by recent research and surveys
conducted on organisations.
Growth of Internet users
eMarketer’s estimate (Figure 1.01) of the world’s growth in Internet users continue to show an
upward positive trend. While 2.83 billion (39.5% of total) of the world’s population will be
connected to the Internet by end of 2014, the forecast shows a further 5.1% average yearly
growth until 2018.
While Internet penetration in the world’s mature and developed countries are fairly high, there
is an indication that there are still significant higher-than-average growth to come in emerging
countries in markets such as Asia Pacific (Figure 1.02) and Latin America (Figure 1.03).
Between 2014-2018, high Internet penetration growth rates are expected in countries such as
Indonesia (13.8%), India (9.3%), Mexico (14.1%) and Brazil (7.2%).
Growth of mobile devices
The other significant contributor to Big Data creation is the growth of mobile devices and its
users. eMarketer estimates that the growth of smartphone users from 2014 to 2018 will be 12%
globally (Figure 1.04). Growth rates are even more remarkable in developing and emerging
economies such as India (126%), Indonesia (107%), Brazil (88%), Mexico (86%) and Argentina
(57%) (Figures 1.05 & 1.06)
Additionally, Forrester Research (2013), in its “Global Business and Consumer Tablet Forecast
Update 2013-2017”, indicated that a substantial increase in the use and ownership of tablet
computers will happen in the next few years. While tablet users globally are estimated at 494

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million in 2014, this number will be 905 million by 2017, representing an 83% growth rate
(Figure 1.07).
Common uses and challenges of Big Data
While the merits of Big Data are volubly advocated by industry leaders, practitioners and the
media, significant challenges still exist in the adoption and implementation of Big Data
capabilities. In Figure 1.08, a Robert Half Technology research shows that 76% of companies
that collect customers’ data do not generate strategic reports or insights and only 46% of
companies who collect data and have the abilities or resources to generate strategic reports or
insights actually do so. This clearly alludes to Gobbles (2013) and Varian’s (2014) position that
contemporary organisations’ ability to exploit Big Data is limited due to either the lack of
internal expertise to perform analysis or talent to make innovative decisions based on Big Data
analyses.
While there are standardisations of collection and storage of Big Data, getting real commercial
value out of Big Data varies between each implementation or business function.
In online retail, the use of Big Data to personalise customer experience when browsing an e-
commerce site increases the likeliness of online buyers performing actions that are beneficial to
the business (Janrain, 2012). When onsite personalisation is effected (Figure 1.09):
• 50% of online buyers are more likely to revisit the website
• 46% of online buyers are likely to make a purchase from the website
• 38% will recommend website to others
• 33% will make an in-store purchase
The above indicates the relative effectiveness of using Big Data (through personalisation
tactics) to gain business results.
Contemporary digital marketers surveyed by Radar Research (2012) overwhelmingly indicated
the top 3 reasons for using Big Data which support the digital marketing theories of Peterson et
al. (2010) and Edwards (2013) (Figure 1.10):
• To drive ROI (64%)
o Peterson et al. (2010) believe that digital marketing is truer to the ‘goal of every
marketing effort’ which is to ‘drive profit’ for an organisation
• To more effectively centralise, organise and leverage own (or client’s) first party online
and offline audience data for targeting (60%)
o Digital marketers in the survey and Experian (2014) agree the need of a
“centralised location” to reconcile multiple data assets to form “an accurate
view of the customer” therefore further demonstrating the importance of a Big
Data solution to drive digital marketing

40
• To create a targeting profile of audience (55%)
o Edwards (2013) outlines the importance of this Big Data ability as a crucial
component of digital marketing, specifying: “Through the collection, storage
and analysis of captured customer data, the marketer now has the ability to
understand who the customer actually is and how he or she behaves.”
Implications for marketers
The above arguments clearly reflect that the trend in Big Data adoption is not merely a
“buzzword” but rather indicative of a need to seriously consider the implications of not
investing in Big Data capabilities, especially for digital marketers. Without the ability to harness
Big Data, the likelihood of digital marketers in unlocking the full capabilities of marketing
automation or programmatic will be limited at best.
While challenges remain in the adoption and implementation of Big Data programs, forward-
looking marketers must endeavour to enhance their abilities to gain advantage through Big Data
technologies. This is especially so for organisations operating in emerging or developing
markets where the number of Internet users and the use of mobile devices are expected to grow
exponentially. This is also an area of growth for mature enterprise organisations with deep
resources to remain competitive in the marketplace as long as sufficient investments in
infrastructure and people expertise are made to exploit Big Data possibilities.
Digital marketing
Growth in digital advertising
According to eMarketer, global spend in digital advertising is expected to grow by 56%
between 2014 and 2018 (Figure 2.01). However, forecasts at region and country levels are
showing a downward trend in growth of digital ad spending in many countries. For example,
while Indonesia is currently exhibiting high growth of digital ad spend in 2014 (75%), this is
expected to slow down to 50% in 2018 (Figure 2.02). In Latin America, Mexico is showing a
similar trend where 2014 growth of 35% will be reduced to 18% in 2018 (Figure 2.03).
Similarly in Middle East and Africa where growth is expected to slow from 39% in 2014 to a
forecasted 20% in 2018 (Figure 2.04).
However, digital ad spending as a percentage of total media will continue to grow (Figure 2.01).
Marketers are expected to commit and increase their digital ad spend as percentage of total
media spending from 26% in 2014 to 33% in 2018.
This clearly shows that, while there is a downward trend in terms of growth for digital
advertising (very likely due to continued establishment of digital infrastructure and connectivity
that takes place all over the global marketplace), digital media channels are gaining more
importance over the years compared to other forms of media.

41
As earlier established, the expected growth of Internet users and mobile devices is also expected
to drive the digital advertising trend. Moreover, eMarketer’s forecast of mobile Internet
advertising spending between 2014 and 2018 is a remarkable 242% (Figure 2.04).
Digital ad format wars
Other forms of Internet advertising format are also closely following the growth of mobile
Internet advertising. With the exception of the stagnating growth of search advertising (12%),
Wells Fargo estimates that 2014 growth rates for online video and mobile stand at 30%,
respectively (Figure 2.05). eMarketer’s forecast for social network advertising also shows a
50% growth in 2014 and expected 30% in 2015.
The evidence and expert forecasts are clearly showing the growth of digital media channels.
This affirms Peterson et al.’s (2010) assessment that “ marketers are abandoning traditional
media at a shocking rate”. With CMOs increasingly focused on efficiencies and effectiveness in
marketing activities, another driver of this “abandonment of traditional media”, as Peterson et
al. states, could be digital marketing’s edge in answering “where those profits come from”.
While marketers are at prerogative to stick to tried-and-tested channels, such as billboards, TV
and print advertising, the growth forecast in digital advertising channels indicates share-of-mind
or share-of-voice opportunities for brand marketers. Therefore, to take advantage of this trend,
the marketing organisation must quickly ramp up its digital marketing knowledge and optimise
its abilities towards marketing automation and programmatic.
Accordingly, the “digital ad format wars” clearly shows the rise of the different channels of
digital media. This insight shows an increased importance for marketers to understand the non-
linear customer persona in order to deliver the right messaging and tactics to this increasingly
typical customer persona.
Marketing automation
Growth in adoption
In ExactTarget’s “2014 State of Marketing” report (2014), the worldwide survey showed that,
among the varied tactics available to marketing professionals, 60% of respondents cite an
increase in their 2014 digital marketing budget for marketing automation, placing it ahead of
email marketing (58%) and social media marketing (57%) (Figure 3.01). eConsultancy, through
its 2013 and 2014 “Marketing Budgets” survey also showed that investments in marketing
automation among marketers were set to increase from 27% in 2013 to 30% in 2014 (Figure
3.02 & 3.03)

42
Additionally, IDC’s 2013 poll of senior technology marketers worldwide also placed marketing
automation (35%) ahead of advertising (31%) and social marketing (28%) in terms of marketing
programs with the most expected growth (Figure 3.04).
Usage levels
While there is certainly discernible growth in investment in marketing automation, comparable
worldwide researches by Ascend2 are showing that marketers are still trying to better
understand marketing automation and making tentative moves. In 2013, 34% of marketers
polled said they “do not use” marketing automation (Figure 3.05). This changed in 2014 where
31% are now saying they do not use marketing automation, reflecting an increase in adoption
and usage levels (Figure 3.06). Conversely, the number of marketers reporting “limited use” of
marketing automation has grown to 53% in 2014 as compared to 44% in 2013.
Adoption by company type
To better understand the types of companies that deploy marketing automation, the Pardot
report “The State of Demand Generation 2013” provides an interesting insight. In its survey of
400 companies worldwide, it is found that only 25% of enterprise-level companies have adopted
marketing automation (Figure 3.07). On the other hand, 76% of service-as-a-companies (SaaS)
deploy marketing automation in their operations. Additionally, 66% of SMB and 59% of mid-
market companies use marketing automation.
The plausible explanation for this is that demand generation is not a focus for enterprise-level
organisations that have achieved scale in market share and brand exposure. However, SaaS
companies, by virtue of operating online as a business model, require marketing automation as a
critical business driver. It can be critically argued that SMB and mid-market companies’ need
for marketing automation is in line with Newberry et al.’s proposal for a robust automation
solution that drives increased productivity, increased productivity or visibility and cost savings
(2008).
Another noteworthy insight is from Ascend2’s “Inbound Marketing” research. It found that not
much difference can be seen on whether a B2B or B2C are more likely to use marketing
automation to manage inbound marketing (Figure 3.08).
Benefits expected vs practical usage
Marketing leaders and CMOs surveyed for CMO Council’s “State of Marketing 2014: A global
and multi-regional marketing assessment” overwhelmingly listed ‘improved efficiency and
campaign effectiveness’ as the top most expected gain (69%) from a marketing automation
investment (Figure 3.09). ‘Greater volume and quality of opportunities/prospects’ (32%) and
‘greater organisational yield and accountability’ (31%) rounded up the top three while ‘higher
conversion and closure rates’ (12%) is further down the list of benefits expected.

43
While the marketing leaders’ expectations align with the marketing automation philosophy as
proposed by Newberry et al. (2008), a separate study by ExactTarget (“2014 State of
Marketing”) indicates that marketing professionals’ top 2014 priorities are more associated with
revenue generation (‘driving increased conversion rates’ – 47%) and gaining brand equity
(‘increasing and improving brand awareness – 46%) (Figure 3.10).
This potential disconnect between marketing leaders and organisational contributors was
emphasised earlier in this discussion through Shaw’s view on automating marketing (2000). To
achieve both goals of maximising shareholder value and to position marketing as ‘the most
important driver of sustained growth’, Shaw believes it is crucial that marketers in an
organisation align and decide on how to use marketing automation technology so that the
objective of usage supports fundamental business goals. This advice will clearly support efforts
for the CMO to ‘cement’ its place in the Top Management Team (Abernathy et al., 2013) and
Kotler et al. (2012) persuasion that a CMO who has high control and influence in decision-
making will deliver results and encourage a forward-looking organisation.
For marketers, it is growing apparent the importance that has been placed by their contemporary
counterparts on marketing automation as seen through increased investment, higher digital
marketing budget allocation and increased usage levels.
However, additional considerations, such as organisation type and usage levels, must be made
to ensure maximum value can be achieved from marketing automation investment. Crucially,
the CMO or senior marketing leaders must communicate and be clear of what is to be achieved
with a marketing automation platform so that organisational expectations are met and that
marketing’s profile within the organisation can be elevated as a result of well-thought strategy.
Programmatic marketing
With all these evidences of usage growth in Big Data, digital marketing and marketing
automation, it is more apparent now that there is a solution need to manage these platform
intelligently, seamlessly, efficiently and effectively. The next discussion highlights the
prospective solution that is programmatic marketing.
Growth in programmatic markets
According to Magna Global’s “The International State of Programmatic” (2014), the world’s
programmatic markets are collectively growing at 39% this year (Figure 4.01). While North
America (57%) leads the world in terms of development in programmatic, Asia Pacific (18%)
and Western Europe (16%) have a substantial hold on the total market.
The growth forecast in terms of revenue is also showing similar upward potential for
programmatic in North America and the rest of the world (Figure 4.02). In the US,

44
programmatic revenue is expected to grow by 73% in 2017 compared to 2014. The outlook is
even more positive globally as the growth forecast expects a 104% change.
In terms of advertising medium, mobile has gained the most ground in the last year. Turn, a
global demand-side technology provider for media buying, reports a 109% year-on-year growth
for mobile on its programmatic platform (Figure 4.04). Video advertising sees a high growth of
65% while display and social both saw a modest growth of 20%.
Programmatic premium
On an even more positive note, premium programmatic (a form of programmatic buying that
does not utilise open advertising trading exchanges; advertisers are offered direct exclusive
deals with publishers using programmatic technology) is expected to show exponential growth.
IDC’s “Forward Markets 2013-2018: Moving direct display ad sales onto RTB platform”
(2014) forecasts that, in the US alone, this segment of the market will grow from a US$675
million business in 2014 to US$9.4 billion in 2018 (Figure 4.03).
This trend is noticeably supported by data from Digiday and The Trade Desk in their ‘The
Future of the Programmatic Forward Market” report (2014). Average advertisers spend value on
premium programmatic inventory has moved from the tentative efforts of 2013 (34% of
advertisers spent 0-10% of their budget on premium programmatic) to bigger budget
commitment in 2014 (almost 50% of advertisers have spent between 20-50% of their
advertising budget on premium programmatic inventory) (Figure 4.03).
Programmatic philosophy
The evidence that programmatic supports the ideals of both digital marketing and marketing
automation philosophies (as espoused by Peterson et al.’s (2010) and Newberry et al. (2008),
respectively) can be found in the survey of advertisers conducted by Winterberry Group and
IAB (2013). Contemporary marketers indicated in the poll (Figure 4.04) that they are drawn to
programmatic due to its ability to deliver these top three objectives:
• More efficiently target consumers across digital media – 55%
• Efficiently value and transact digital media – 49%
• Improve operational efficiency – 33%
Further suggestion that programmatic techniques are in line with the aspirations of executive
marketers for their marketing organisations is presented in the same Winterberry Group and
IAB research paper. When asked which are the essential programmatic approaches they are
planning to use or to continue using in the next 2 years (Figure 4.05), the top responses from
these senior marketers allude to a comprehensive and strategic approach that maximises the
value of their Big Data as well as employing optimisation and analytical techniques to gathered
data. The top responses include:

45
• Audience segmentation – 91%
• Insight development – 88%
• Automation of back-end processes – 86%
• Utilisation of an auction-based approaches to media buying – 86%
The documented evidence of growth in the programmatic markets goes to show that
programmatic marketing has reached a turning point in disrupting traditional paid media
through innovation and technology. Contemporary marketers will do well to take heed in
understanding this new advertising and marketing landscape in order to remain at the forefront
of their trade.
There is also a distinct correlation between growth of mobile and the growth of programmatic
media buying as evidenced in Turn’s data. For marketers operating in or planning to engage
emerging and developing markets, the gilded opportunity to penetrate these markets may well
come from the ability to master and utilise programmatic techniques in order to reach targeted
audiences.
Most critically, research results have shown that programmatic marketing closely aligns itself
with contemporary practices and aspirations of today’s forward-looking and innovative CMOs
and marketing leaders.

46
The Micro View
Programmatic marketing as a solution for corporate organisations to maintain
competitiveness and relevance in the marketplace
The previous discussion has established the assumption that today’s marketers are thinking of
Big Data, digital marketing, marketing automation and programmatic marketing as present-day
areas of interest and innovation platforms for progress. The following discussion will focus on
answering operational questions on how firms have benefitted from adopting programmatic
marketing, the limitations and barriers to adoption and issues such as those concerning data
privacy and security.
Programmatic positives
The programmatic marketing vision can be best analysed through the following case studies of
innovative programmatic practices in the industry.
Case study: Ad effectiveness
ING, a European banking institution, engaged BannerConect (a programmatic media buying
company) through its media agency, MindShare, to create “environmentally targeted” ads
(AppNexus, 2013).
Using AppNexus’s real-time advertising platform, BannerConnect created a “real-time weather”
data feed that was integrated with AppNexus’ network of online advertising inventory. Three
sets of advertising creative were created: two sets were designed to reflect the current weather
and the other was a generic control set. Using real-time weather information on a specific
geolocation, the objective was to serve customised online banner advertisements to website
visitors that come with messaging that replicate the actual weather outdoors (Figure 5.01).
The results from the campaign were encouraging in terms of showcasing programmatic’s
capabilities. The customised advertisements performed 28% better than the control ads in
delivering the desired results for ING.
This case study demonstrates Said’s assertion that programmatic’s value is in its ability to
“match marketers to audiences, at a large scale, in real-time…” hence resulting in effectiveness
and efficiencies of delivering marketing messages and acquiring anticipated outcomes (Said,
2014).

47
Case study: Ad personalisation
The Container Store, a US multi-channel retailer of home storage and organisation solutions,
has more than 10,000 unique products on its e-commerce site. Before partnering with MyBuys,
an automated personalisation solutions provider, The Container Store created manual product
recommendations on its own, a tedious approach that was done with limited customer data.
Also, due to the multifunctional nature of their products and the scale of variety, it is always not
the case of “finding the right product in the right place” (eMarketer, 2012).
Working with MyBuys’ algorithm, a recommendation engine was built that looked at multiple
data sets including (but not limited to) browsing behaviour, clicks on items, wish list, shopping
carts, product categories and price points. Where available, CRM data, such as in-store
purchases, was also integrated in the engine.
The output of this Big Data project was visible in multiple formats. On their website, customers
are able to see product recommendations on product detail, shopping cart and order
confirmation pages. Customers also receive recommendations in transactional emails such as
order confirmation and shipping confirmation.
With the recommendation engine in place, the average order value has risen by 39% on average
and conversion rates are three times more for customers who interact with recommendations as
compared to those who does not. Additionally, the open rate for product recommendation
emails is 40% higher than regular emails.
The results that The Container Store achieved through this project prove Manyika et al.’s
(2011) theory that, through Big Data techniques, micro-segmented audiences are more likely to
be receptive to specific and real-time advertising messages.
Case study: Mobile audiences at global scale
The fashion retailer, Forever 21, was looking to gain advantage of its audience on mobile. The
objectives were to scale their online business, drive more return on advertising spend and
increase revenue through paid channels (Sociomantic, 2014). By engaging Sociomantic, a
provider of streaming CRM platform for mobile, and Google’s DoubleClick Ad Exchange
(AdX), Forever 21 was able to realise these objectives.
Working with AdX, Forever 21 was able to access mobile advertising inventory on a global
scale that works across browsers, apps and devices. The Sociomantic CRM platform
complements this by providing the ability to create personalised mobile advertisements in real
time using Forever 21’s first party data on their customers. This data set includes CRM, loyalty
and yield management information. Together, the integration allows Forever 21 to identify and
market to its own customers across the world through their mobile device.
The key result of this campaign is the 20% higher click through rate that Forever 21 achieved

48
on mobile devices as compared to campaigns targeting desktop users. Also, the mobile
campaign achieved 15% more volume while maintaining the same amount of return on
advertising investment (ROAS).
Through this case study, it is shown that the one-to-one marketing concept by Peppers and
Rogers (1993) are already being realised on a large scale through innovative uses of Big Data
and digital marketing channels. The increase in campaign volume also shows the effect of
widespread mobile lifestyle adoption among consumers that have benefitted marketers.
Limitations and barriers
Organisational readiness
Among the top challenges that senior marketing leaders face are the collection and management
of structured and unstructured data (61%), deploying the correct CRM system to manage client
or customer relationships effectively (48%) and determining ROI of marketing activities
through tracking and measuring the effectiveness of integrated marketing campaigns (44%)
(Experian, 2014) (Figure 6.01).
The Experian Marketing Services’ global study also found that the top three barriers to enable
cross-channel marketing in programmatic for marketers are company’s current technology
(39%), inherent organisational structure (38%) and budget (38%) (Figure 6.02).
Automation obstacles
A similar situation is noticed where marketing automation is concerned. A survey of marketers
by Regalix (2014) found that 65% of respondents allocated less than 10% of their marketing
budget to automation. 25% spent between 10-15% while only 10% of marketers allocated 15-
25% of their budget to marketing automation (Figure 6.03)
Getting the most out of marketing automation is also proving to be a challenge for
contemporary marketers. In the same survey (Figure 6.04), when asked what would be the
“Biggest obstacles to effective use of marketing automation”, the top 5 responses were:
• Lack of resources – 55%
• Lack of time – 53%
• Lack of expertise to use the data and intelligence offered by the tool – 47%
• Lack of clearly defined strategy – 45%
• Poor infrastructure to collect and analyse data – 36%
Inferring the above researches and data, it shows that there is still a lack of confidence regarding
marketing automation in the market hence the tentative investments even though digitally-
driven business operation is pervasive everywhere. With deficiencies in talent, strategy and
infrastructure, it is also clear that there is a shortage of strategic leadership to define the way

49
forward with marketing automation. Hence, Shaw’s insistence that people still drives marketing
innovation instead of machines is to be noted (2000).
The Data Dilemma
Data accuracy is also cited as one of the challenges of implementing programmatic marketing.
While 93% of organisations around the world believe data is essential for marketing success, as
much as 22% of these organisations believe that their data is wrong (Experian, 2014).
In fact, 59% of senior marketers believe that data inaccuracy can be attributed to human error.
The other two most cited reasons for data inaccuracy are a ‘lack of internal communications
between departments’ and ‘inadequate data strategy’ (Figure 6.05).
Negative programmatic
Resistance to change
When brand advertisers were polled on their views on what are the “top pain points” in the
adoption or use of programmatic marketing by their organisations, the clear issue that dominates
is one of ‘brand safety’ ie. The lack of control on where their brand or advertising messages will
appear when purchased programmatically via real-time bidding (Winterberry Group, 2013)
(Figure 7.01). While this issue has been addressed by technology providers through built-in
controls, for example, MediaMath’s partnership with Peer39 (Peer39, 2011), concerns remain.
The other top concerns expressed by marketers are:
• Brand safety – 33%
• Lack of transparency in programmatic techniques – 24%
• Resistance from current process owners – 18%
• Internal process/marketing ops challenges – 18%
Interestingly, two of the top concerns mentioned above are non-marketing and non-technology
related. Push back from current process owners (“…our sales team is standing in the way of our
attempts to do more with programmatic. They’re threatened by what technology can do.”) and
challenges posed by internal processes or operations (“All we need is help building the business
case for what this helps us accomplish with the customer. Oh, and then we have to tear down
processes that were built over 50 years ago.”) are organisational or management issues that can
be overcome through leadership and education (Winterberry Group, 2013). This is an ideal
opportunity for Patton and Finlay’s ‘evolved CMO’ (2013), an empowered and influential
personality, to step in and encourage and motivate the organisation towards innovation and
growth.
Privacy concerns
According to Forbes Insights and Turn’s “The Promise of Privacy” report (2013), while online
tracking cookies remain the most popular form of data collection for marketers (Figure 7.03),

50
other forms of data collection and customer tracking, especially mobile, are on the rise. The lure
of mobile data is especially strong as it provides marketers with the opportunity to engage
customers in location-based targeting, such as identifying a ‘business traveller’ through a record
of mobile device presence in three different airports in the space of two weeks. However, this
trend in collecting, storing and marketing against a customer’s personal data will continue as
digital marketing continues to grow and mature.

MBA Dissertation: "Investigation into Programmatic Marketing as a More Efficient, Responsible and Financially Prudent Digital Marketing Practice of the Future"

MBA Dissertation: "Investigation into Programmatic Marketing as a More Efficient, Responsible and Financially Prudent Digital Marketing Practice of the Future"

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MBA Dissertation: "Investigation into Programmatic Marketing as a More Efficient, Responsible and Financially Prudent Digital Marketing Practice of the Future"